A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New study shows FTRL mechanism works with correlated events.
Paper studies estimating asset correlations across sectors.
Model predicts epileptic seizures with high accuracy using EEG signals.
A deep neural network detects sleep events in polysomnograms with high accuracy.
Modeling solar ramping events with spatio-temporal point processes.
Long-range correlation in financial time series reflects the complex dynamics of the stock markets driven by algorithms and human decisions. Our analysis exploits ultra-high frequency order book data from NASDAQ Nordic over a period of three years to numerically estimate the power-law scaling exponents using detrended …
Python tool detects economic crises from S&P500 correlation data.
Study uncovers financial trends from cross-lingual news data.
We propose two structural models for stochastic losses given default which allow to model the credit losses of a portfolio of defaultable financial instruments. The credit losses are integrated into a structural model of default events accounting for correlations between the default events and the associated losses. We…
Traditional stock market prediction methods commonly only utilize the historical trading data, ignoring the fact that stock market fluctuations can be impacted by various other information sources such as stock related events. Although some recent works propose event-driven prediction approaches by considering the even…
Deep model forecasts correlated multivariate time series.
The distribution of recurrence times or return intervals between extreme events is important to characterize and understand the behavior of physical systems and phenomena in many disciplines. It is well known that many physical processes in nature and society display long range correlations. Hence, in the last few year…
Neural network model predicts alternating event-free periods.
This paper uses MIL and MHCNN-RNN to predict precursors to aviation safety events.
Clusters of financial market states identified over 2006-2019.
Value at risk (VaR) is a risk measure that has been widely implemented by financial institutions. This paper measures the correlation among asset price changes implied from VaR calculation. Empirical results using US and UK equity indexes show that implied correlation is not constant but tends to be higher for events i…
While the long-ranged correlation of market orders and their impact on prices has been relatively well studied in the literature, the corresponding studies of limit orders and cancellations are scarce. We provide here an empirical study of the cross-correlation between all these different events, and their respective i…
Previous work has shown that popular trending events are important external factors which pose significant influence on user search behavior and also provided a way to computationally model this influence. However, their problem formulation was based on the strong assumption that each event poses its influence independ…
Financial markets, being spectacular examples of complex systems, display rich correlation structures among price returns of different assets. The correlation structures change drastically, akin to phase transitions in physical phenomena, as do the influential stocks (leaders) and sectors (communities), during market e…
ProxiModel extracts high-quality news events from news corpora.
Financial event studies often misestimate causal effects due to misspecified factor models.
We present a new volatility model, simple to implement, that includes a leverage effect whose return-volatility correlation function fits to empirical observations. This model is able to capture both the "retarded effect" induced by the specific risk, and the "panic effect", which occurs whenever systematic risk become…
New method identifies precursors of financial crises in market correlation structures.
We study the structure of locational marginal prices in day-ahead and real-time wholesale electricity markets. In particular, we consider the case of two North American markets and show that the price correlations contain information on the locational structure of the grid. We study various clustering methods and intro…
Traditional stock market prediction approaches commonly utilize the historical price-related data of the stocks to forecast their future trends. As the Web information grows, recently some works try to explore financial news to improve the prediction. Effective indicators, e.g., the events related to the stocks and the…
The paper develops a test for independence of selected Gaussian variables after thresholding correlations.
Model simulates correlation emergence in two coupled limit order books.
Anticipatory model generates music with control over events.
SurvSurf predicts first hitting times for intermittent events without monotonic violations.
Review of correlation-based financial networks and entropy measures.
Versatile model for High Energy Physics events.
The instability of historical risk factor correlations renders their use in estimating portfolio risk extremely questionable. In periods of market stress correlations of risk factors have a tendency to quickly go well beyond estimated values. For instance, in times of severe market stress, one would expect with certain…
The P300 Brain-Computer Interface (BCI) is a well-established communication channel for severely disabled people. The P300 event-related potential is mostly characterized by its amplitude or its area, which correlate with the spelling accuracy of the P300 speller. Here, we introduce a novel approach for estimating the …
The availability of a large amount of electronic health records (EHR) provides huge opportunities to improve health care service by mining these data. One important application is clinical endpoint prediction, which aims to predict whether a disease, a symptom or an abnormal lab test will happen in the future according…
A new methodology for incorporating LGD correlation effects into the Basel II risk weight functions is introduced. This methodology is based on modelling of LGD and default event with a single loss variable. The resulting formulas for capital charges are numerically compared to the current proposals by the Basel Commit…
The problem to accurately and parsimoniously characterize random series of events (RSEs) present in the Web, such as e-mail conversations or Twitter hashtags, is not trivial. Reports found in the literature reveal two apparent conflicting visions of how RSEs should be modeled. From one side, the Poissonian processes, o…
Study quantifies systemic risk in DeFi using network analysis.
Models predict fire and other emergencies in Edmonton.
Enhances Fourier estimator performance for asynchronous event-data.
Method captures shared information across many views robustly.
We investigate the daily correlation present among market indices of stock exchanges located all over the world in the time period Jan 1996 - Jul 2009. We discover that the correlation among market indices presents both a fast and a slow dynamics. The slow dynamics reflects the development and consolidation of globaliz…
The Epps effect helps distinguish between continuous and discrete financial tick data.
The paper uses machine learning to find causal rules from business process logs.
This paper evaluates data enrichment techniques for rare event detection in manufacturing.
Stock correlations is crucial to asset pricing, investor decision-making, and financial risk regulations. However, microscopic explanation based on agent-based modeling is still lacking. We here propose a model derived from minority game for modeling stock correlations, in which an agent's expected return for one stock…
New method clusters hydrological and sediment data for storm event analysis.
The paper analyzes heavy-tailed multivariate distributions in non-stationary systems using random matrix theory.
Bitcoin's integration with major financial indices intensifies, suggesting a shift from alternative to integrated asset.